Here, f is the activation function, \(i^{next}\)  is the input data for the next hidden layer, weight of edge connected to previous output and next layer input is defined in \(w^{next}_n\) , \(o_m\)  contains output value of one cell and \( b^{next}\)  contains bias. For feature extraction the stacked LSTM proves to improve the extraction process[1]}.
